面向对象的GF-1遥感影像多尺度分割研究
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摘要
【目的】探索基于GF-1遥感影像的干旱区绿洲地物的最优分割尺度,为民勤绿洲地物的高精度提取奠定基础.【方法】本研究采用了综合类内一致性与类间异质性两种指数构建的RMAS分割指数,以国产GF-1遥感影像为数据源,民勤绿洲为试验区,获取了建筑物、林地与耕地的最优分割尺度;通过参考对象与分割对象之间的光谱与位置关系,提出了使用光谱相似性指数SSI以及分割距离指数SDI对分割质量进行评价;使用最邻近分类法对试验区地物进行提取.【结果】1)试验区建筑物、林地以及耕地的最优分割尺度为50、30、80;2)最优分割尺度下,SSI指数接近0,且SDI指数最小;3)地物提取的总体精度为87.50%,Kappa系数为84.35%,3种地物在最优分割尺度下提取的精度分别为82.86%、89.29%、96.15%.【结论】通过RMAS指数能够准确获取最优分割尺度,以达到较高的地物提取精度.
【Objective】To explore the optimal segmentation scale of the arid region based on GF-1 remote sensing image,and lay the foundation for the high accuracy extraction of the ground objects of Minqin oasis.【Method】The RMASsegmentation index was constructed by combining interclass consistency and interclass heterogeneity,which was used to obtain the optimal segmentation scale of the building,woodland and cultivated land.GF-1 remote sensing image was used as the data source.Considering the relationship between spectral and position,the spectral similarity index(SSI)and the segmentation distance index(SDI)were used to evaluate the segmentation result.The nearest neighbor classification was used to extract the features of the research area.【Result】The optimal scale was 50,30 and 80 respectively.In the optimal segmentation scale,the SSIindex was close to 0 and the SDIindex was the smallest.The overall classification accuracy was 87.50%,and the kappacoefficient was 84.35%.The producer accuracy of the building,woodland and cultivated land were 82.86%,89.29% and 96.15% respectively.【Conclusion】The optimal segmentation scale could be accurately obtained by RMASindex for high extraction accuracy.
【Objective】To explore the optimal segmentation scale of the arid region based on GF-1 remote sensing image,and lay the foundation for the high accuracy extraction of the ground objects of Minqin oasis.【Method】The RMASsegmentation index was constructed by combining interclass consistency and interclass heterogeneity,which was used to obtain the optimal segmentation scale of the building,woodland and cultivated land.GF-1 remote sensing image was used as the data source.Considering the relationship between spectral and position,the spectral similarity index(SSI)and the segmentation distance index(SDI)were used to evaluate the segmentation result.The nearest neighbor classification was used to extract the features of the research area.【Result】The optimal scale was 50,30 and 80 respectively.In the optimal segmentation scale,the SSIindex was close to 0 and the SDIindex was the smallest.The overall classification accuracy was 87.50%,and the kappacoefficient was 84.35%.The producer accuracy of the building,woodland and cultivated land were 82.86%,89.29% and 96.15% respectively.【Conclusion】The optimal segmentation scale could be accurately obtained by RMASindex for high extraction accuracy.
引文
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[20]陈文倩,丁建丽,李艳华,等.基于国产GF-1遥感影像的水体提取方法[J].资源科学,2015,37(6):1166-1172.
[21]宋晓阳,申文明,万华伟,等.基于高分遥感的可可西里自然保护区藏羚羊生境适宜性动态监测[J].资源科学,2016,38(8):1434-1442.
[22]李艳华,丁建丽,闫人华.基于国产GF-1遥感影像的山区细小水体提取方法研究[J].资源科学,2015,37(2):408-416.
[23]王荣,李静,王亚琴,等.面向对象最优分割尺度的选择及评价[J].测绘科学,2015,40(11):105-163.
[24]李鸿儒,王继和,蒋志荣,等.白刺沙包发育过程的土壤水分与根系生物量的关系[J].甘肃农业大学学报,2010,45(6):133-138.
[25]王具元,蒋志荣,王继和,等.民勤绿洲荒漠交错带三种沙丘类型的自然植被特征[J].甘肃农业大学学报,2006,41(2):51-55.
[2]黄威,汪小钦,陈芸芝,等.基于面向对象的平潭岛大比例尺森林资源监测方法[J].遥感技术与应用,2014,29(1):138-143.
[3]王荣,江东,韩惠,等.高分辨率遥感影像天然林与人工林植被覆盖信息提取[J].资源科学,2013,35(4):868-874.
[4]武文波,陈静.基于ETM+的遥感影像信息提取研究[J].甘肃农业大学学报,2008,43(5):142-146.
[5]MEINEL G,NEUBERT M.A comparision of segmentation programs for highresolution remote sensing data[J].Remote Sensing and Spatial Information Sciences,2004,35:1097-1102.
[6]HAY G J,CASTILLA G.Geographic object-based image analysis:A new name for a new discipline[M].Berlin Heidelberg:Springer-Verlag,2008:75-90.
[7]李秦,高锡章,张涛,等.最优分割尺度下的多层次遥感地物分类实验分析[J].地球信息科学学报,2011,13(3):409-417.
[8]杨猛,杨树文,雍万铃,等.集成特征分量的阴影多尺度分割方法[J].遥感信息,2017,32(1):109-114.
[9]WOODCOCK C E,STRAHLER A H.The factor of scale in remote sensing[J].Remote Sensing of Environment,1987(21):311-332.
[10]田岩,谢玉波,史文中,等.基于局部方差的多分辨率图像分割方法[J].系统工程与电子技术,2006,28(12):1921-1926.
[11]何敏,张文君,王卫红.面向对象的最优分割尺度计算模型[J].大地测量与地球动力学,2009,29(1):106-109.
[12]胡文亮,赵萍,董张玉.一种改进的遥感影像面向对象最优分割尺度计算模型[J].地理与地理信息科学,2010,26(6):15-18.
[13]LUCIEER A,STEIN A.Existential uncertainty of spatial objects segmented from satellite sensor imagery[J].Transactions on Geoscience and Remote Sensing,2002,40(11):2518-2521.
[14]NEUBERT M,HEROLD H.Assessment of remote sensing image segmentation quality[J].Journal of Remote Sensing,2008.
[15]于欢,张树清,孔博,等.面向对象遥感影像分类的最优分割尺度选择研究[J].中国图像图形学报,2010,15(2):352-360.
[16]鲁恒,李永树,唐敏.面向对象的山地区域多源遥感影像分割尺度选择及评价[J].山地学报,2011,29(6):688-694.
[17]ARNAUD MARTIN,HICHAM LANAYA,ANDREAS ARNOLD-BOS.Evaluation for uncertain image classification and segmentation[J].Patter Recognition,2006,39(11):1987-1995.
[18]王荣,王昭生,刘晓曼.多尺度多准则的遥感影像线性地物信息提取[J].测绘科学,2016,41(11):146-150.
[19]张俊,朱国龙,李妍.面向对象高分辨率影像信息提取中的尺度效应及最优分割尺度研究[J].2011,36(2):107-109.
[20]陈文倩,丁建丽,李艳华,等.基于国产GF-1遥感影像的水体提取方法[J].资源科学,2015,37(6):1166-1172.
[21]宋晓阳,申文明,万华伟,等.基于高分遥感的可可西里自然保护区藏羚羊生境适宜性动态监测[J].资源科学,2016,38(8):1434-1442.
[22]李艳华,丁建丽,闫人华.基于国产GF-1遥感影像的山区细小水体提取方法研究[J].资源科学,2015,37(2):408-416.
[23]王荣,李静,王亚琴,等.面向对象最优分割尺度的选择及评价[J].测绘科学,2015,40(11):105-163.
[24]李鸿儒,王继和,蒋志荣,等.白刺沙包发育过程的土壤水分与根系生物量的关系[J].甘肃农业大学学报,2010,45(6):133-138.
[25]王具元,蒋志荣,王继和,等.民勤绿洲荒漠交错带三种沙丘类型的自然植被特征[J].甘肃农业大学学报,2006,41(2):51-55.